Trabajo 3

# Lectura de la data
data <- readr::read_delim(file = "bank-additional-full.csv", locale = readr::locale(encoding = "UTF-8"), delim = ";")

## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_character(),
##   age = col_double(),
##   duration = col_double(),
##   campaign = col_double(),
##   pdays = col_double(),
##   previous = col_double(),
##   emp.var.rate = col_double(),
##   cons.price.idx = col_double(),
##   cons.conf.idx = col_double(),
##   euribor3m = col_double(),
##   nr.employed = col_double()
## )
## i Use `spec()` for the full column specifications.

# Descripcion de variables
skimr::skim(data)

Data summary

Name	data
Number of rows	41188
Number of columns	21
_______________________
Column type frequency:
character	11
numeric	10
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
job	0	1	6	13	0	12	0
marital	0	1	6	8	0	4	0
education	0	1	7	19	0	8	0
default	0	1	2	7	0	3	0
housing	0	1	2	7	0	3	0
loan	0	1	2	7	0	3	0
contact	0	1	8	9	0	2	0
month	0	1	3	3	0	10	0
day_of_week	0	1	3	3	0	5	0
poutcome	0	1	7	11	0	3	0
y	0	1	2	3	0	2	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
age	0	1	40.02	10.42	17.00	32.00	38.00	47.00	98.00	▅▇▃▁▁
duration	0	1	258.29	259.28	0.00	102.00	180.00	319.00	4918.00	▇▁▁▁▁
campaign	0	1	2.57	2.77	1.00	1.00	2.00	3.00	56.00	▇▁▁▁▁
pdays	0	1	962.48	186.91	0.00	999.00	999.00	999.00	999.00	▁▁▁▁▇
previous	0	1	0.17	0.49	0.00	0.00	0.00	0.00	7.00	▇▁▁▁▁
emp.var.rate	0	1	0.08	1.57	-3.40	-1.80	1.10	1.40	1.40	▁▃▁▁▇
cons.price.idx	0	1	93.58	0.58	92.20	93.08	93.75	93.99	94.77	▁▆▃▇▂
cons.conf.idx	0	1	-40.50	4.63	-50.80	-42.70	-41.80	-36.40	-26.90	▅▇▁▇▁
euribor3m	0	1	3.62	1.73	0.63	1.34	4.86	4.96	5.04	▅▁▁▁▇
nr.employed	0	1	5167.04	72.25	4963.60	5099.10	5191.00	5228.10	5228.10	▁▁▃▁▇

# Seleccion de 4 variables cuantitativas y 1 cualtiativa

fil.data <- 
  dplyr::select(data, c(y, age, duration, emp.var.rate, cons.price.idx)) %>% 
  tidyr::drop_na() %>% 
  dplyr::mutate(y = as.factor(y))
head(fil.data)

Regresion logistica

# Regresion logistica
log.reg <- glm(formula = y ~ ., data = fil.data, family = binomial(link = "logit"))
summary(log.reg)

## 
## Call:
## glm(formula = y ~ ., family = binomial(link = "logit"), data = fil.data)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.9474  -0.3897  -0.1967  -0.1319   3.1785  
## 
## Coefficients:
##                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -1.309e+02  4.112e+00 -31.831  < 2e-16 ***
## age             6.191e-03  1.549e-03   3.998  6.4e-05 ***
## duration        4.520e-03  7.092e-05  63.732  < 2e-16 ***
## emp.var.rate   -1.128e+00  1.920e-02 -58.764  < 2e-16 ***
## cons.price.idx  1.352e+00  4.385e-02  30.835  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 28999  on 41187  degrees of freedom
## Residual deviance: 19067  on 41183  degrees of freedom
## AIC: 19077
## 
## Number of Fisher Scoring iterations: 6

# Regresion logistica: caret
RNGkind(sample.kind = "Rejection")
set.seed(100)
log.reg2 <- caret::train(y ~ ., data = fil.data, method = "glm", family = "binomial")
log.reg2

## Generalized Linear Model 
## 
## 41188 samples
##     4 predictor
##     2 classes: 'no', 'yes' 
## 
## No pre-processing
## Resampling: Bootstrapped (25 reps) 
## Summary of sample sizes: 41188, 41188, 41188, 41188, 41188, 41188, ... 
## Resampling results:
## 
##   Accuracy  Kappa    
##   0.901024  0.3576079

summary(log.reg2$finalModel)

## 
## Call:
## NULL
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.9474  -0.3897  -0.1967  -0.1319   3.1785  
## 
## Coefficients:
##                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept)    -1.309e+02  4.112e+00 -31.831  < 2e-16 ***
## age             6.191e-03  1.549e-03   3.998  6.4e-05 ***
## duration        4.520e-03  7.092e-05  63.732  < 2e-16 ***
## emp.var.rate   -1.128e+00  1.920e-02 -58.764  < 2e-16 ***
## cons.price.idx  1.352e+00  4.385e-02  30.835  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 28999  on 41187  degrees of freedom
## Residual deviance: 19067  on 41183  degrees of freedom
## AIC: 19077
## 
## Number of Fisher Scoring iterations: 6

Analisis descriminante lineal: caret

RNGkind(sample.kind = "Rejection")

# Estandarizacion de variables

pre.Proc <- caret::preProcess(x = fil.data, method = c("center", "scale"))
fil.data.z <- predict(pre.Proc, fil.data)
head(fil.data.z)

# Analisis discrminante lineal

set.seed(100)
lda <- caret::train(y ~ ., data = fil.data.z, method = "lda")
lda

## Linear Discriminant Analysis 
## 
## 41188 samples
##     4 predictor
##     2 classes: 'no', 'yes' 
## 
## No pre-processing
## Resampling: Bootstrapped (25 reps) 
## Summary of sample sizes: 41188, 41188, 41188, 41188, 41188, 41188, ... 
## Resampling results:
## 
##   Accuracy   Kappa    
##   0.9018298  0.4030479

K vecinos mas cercanos (KNN)

RNGkind(sample.kind = "Rejection")

# Estandarizacion de variables

pre.Proc <- caret::preProcess(x = fil.data, method = c("center", "scale"))
fil.data.z <- predict(pre.Proc, fil.data)
head(fil.data.z)

# Eleccion del valor optimo de k

set.seed(100)
knn <- caret::train(y ~ ., data = fil.data.z, method = "knn")
knn

## k-Nearest Neighbors 
## 
## 41188 samples
##     4 predictor
##     2 classes: 'no', 'yes' 
## 
## No pre-processing
## Resampling: Bootstrapped (25 reps) 
## Summary of sample sizes: 41188, 41188, 41188, 41188, 41188, 41188, ... 
## Resampling results across tuning parameters:
## 
##   k  Accuracy   Kappa    
##   5  0.8929313  0.4384832
##   7  0.8971711  0.4513161
##   9  0.8991547  0.4562761
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was k = 9.